[Clinical application study of acupotomy-injection technique with targeted three-point in the treatment of frozen shoulder].
Bibliographic record
Abstract
OBJECTIVE: To observe the clinical efficacy of minimally invasive acupotomy-injection technique with targeted three-point in the treatment of frozen shoulder. METHODS: From March 2017 to November 2018, a total of 140 patients with frozen shoulder were randomly divided into observation group and control group. The observation group was made up of 70 patients, including 30 males and 40 females; the mean age was (59.2±11.5) years old; the mean duration of disease was (6.76±4.14) months; the observed patients were treated with acupotomy-injection technique with targeted three-point. There were also 70 patients in the control group, made up of 29 males and 41 females; the mean age was (58.9±11.8) years old; the mean duration of disease was (6.65±3.98) months; the control group was treated with the small needle knife therapy. Before treatment and one month after the treatment, the pain levels of both groups were assessed using the short-form McGill pain questionnaire, and the shoulder function was evaluated using the Constant-Murley Shoulder Outcome Scoring. The clinical efficacy of between groups was compared after treatment, and finally, the improvement rate of pain degree was used to evaluate the therapeutic effect of the patients. RESULTS: >0.05). In addition, the markedly effective rate of pain improvement was 70.0% and 45.7% in the observation group and the control group, respectively, meanwhile, the corresponding total effective rate was 97.1% and 84.3%, respectively. CONCLUSIONS: The application of acupotomy-injection technique with targeted three-point in the treatment of frozen shoulder shows definite efficacy, easy operation, little pain and high safety. Therefore, it is an ideal method for minimally invasive treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".